Image blur is an undesirable degradation that often accompanies the image formation process due to factors such as camera shake. Blind deconvolution is an image deblurring technique that permits recovery of a sharp image from a single blurry observed image in the presence of a poorly determined or unknown point spread function. The point spread function, also referred to in this disclosure as a blur kernel, is useful in digital image processing for processes such as sharpening, blurring, and edge-detection, to name a few examples. The blur kernel represents an impulse response of a focused optical system, and must be estimated to obtain a deblurred image using blind deconvolution. Some existing blind deblurring methods do not work well in the presence of noise, which can lead to inaccurate blur kernel estimation. Moreover, it has been empirically observed that even for noise-free images, image structures with a scale, or resolution, smaller than that of the blur kernel can cause large kernel estimation errors. To account for such so-called small scale structures, various structure selection techniques, such as hard/hysteresis gradient thresholding, selective edge map, and image decomposition may be incorporated into kernel estimation. However, such structure selection techniques do not adapt well to images with different scale structures. Therefore, an improved scale adaptive blind deblurring technique is needed.